def ImageInMultiROIOut2D(root_folder, input_shape, batch_size=8, hierarchical_level=0, augment_param={}): input_number, output_number = _GetInputOutputNumber(root_folder) assert (output_number == 1) case_list = os.listdir(root_folder) input_list = [[] for index in range(input_number)] output_list = [[] for index in range(hierarchical_level + 1)] param_generator = AugmentParametersGenerator() augmentor = DataAugmentor2D() while True: shuffle(case_list) for case in case_list: case_path = os.path.join(root_folder, case) if not case_path.endswith('.h5'): continue input_data_list, output_data_list = [], [] file = h5py.File(case_path, 'r') for input_number_index in range(input_number): temp_data = np.asarray(file['input_' + str(input_number_index)]) if temp_data.ndim == 2: temp_data = temp_data[..., np.newaxis] input_data_list.append(temp_data) one_roi = np.asarray(file['output_0']) param_generator.RandomParameters(augment_param) augmentor.SetParameter(param_generator.GetRandomParametersDict()) input_data_list = _AugmentDataList2D(input_data_list, augmentor) one_roi = _AugmentDataList2D([one_roi], augmentor)[0] input_data_list = _CropDataList2D(input_data_list, input_shape) one_roi = _CropDataList2D([one_roi], input_shape)[0] one_roi_list = [one_roi] for index in np.arange(1, hierarchical_level + 1): temp_roi = deepcopy( cv2.resize(one_roi, (one_roi.shape[0] // np.power(2, index), one_roi.shape[1] // np.power(2, index)), interpolation=cv2.INTER_LINEAR)) one_roi_list.insert(0, temp_roi[..., np.newaxis]) _AddOneSample(input_list, input_data_list) _AddOneSample(output_list, one_roi_list) if len(input_list[0]) >= batch_size: inputs = _MakeKerasFormat(input_list) outputs = _MakeKerasFormat(output_list) yield inputs, outputs input_list = [[] for index in range(input_number)] output_list = [[] for index in range(hierarchical_level + 1)]
def ImageInImageOut2D(root_folder, input_shape, batch_size=8, augment_param={}): input_number, output_number = _GetInputOutputNumber(root_folder) case_list = os.listdir(root_folder) input_list = [[] for index in range(input_number)] output_list = [[] for index in range(output_number)] param_generator = AugmentParametersGenerator() augmentor = DataAugmentor2D() crop = ImageProcess2D() while True: shuffle(case_list) for case in case_list: case_path = os.path.join(root_folder, case) if not case_path.endswith('.h5'): continue input_data_list, output_data_list = [], [] try: file = h5py.File(case_path, 'r') for input_number_index in range(input_number): temp_data = np.asarray(file['input_' + str(input_number_index)]) if temp_data.ndim == 2: temp_data = temp_data[..., np.newaxis] input_data_list.append(temp_data) for output_number_index in range(output_number): temp_data = np.asarray(file['output_' + str(output_number_index)]) if temp_data.ndim == 2: temp_data = temp_data[..., np.newaxis] output_data_list.append(temp_data) file.close() except Exception as e: print(case_path) print(e.__str__()) continue param_generator.RandomParameters(augment_param) augmentor.SetParameter(param_generator.GetRandomParametersDict()) input_data_list = _AugmentDataList2D(input_data_list, augmentor) output_data_list = _AugmentDataList2D(output_data_list, augmentor) input_data_list = crop.CropDataList2D(input_data_list, input_shape) output_data_list = crop.CropDataList2D(output_data_list, input_shape) _AddOneSample(input_list, input_data_list) _AddOneSample(output_list, output_data_list) if len(input_list[0]) >= batch_size: inputs = _MakeKerasFormat(input_list) outputs = _MakeKerasFormat(output_list) return inputs, outputs
def AugmentScript(): from MeDIT.SaveAndLoad import LoadNiiData from MeDIT.Visualization import Imshow3DArray, DrawBoundaryOfBinaryMask from MeDIT.Normalize import Normalize01 import numpy as np import time t2_image, _, t2 = LoadNiiData(r'H:/data/TZ roi/BIAN ZHONG BEN/t2.nii', dtype=np.float32) roi_image, _, roi = LoadNiiData( r'H:/data/TZ roi/BIAN ZHONG BEN/prostate_roi_TrumpetNet.nii', dtype=np.uint8) Imshow3DArray(t2, ROI=roi) t2_slice = t2[..., 10] roi_slice = roi[..., 10] # DrawBoundaryOfBinaryMask(t2_slice, roi_slice) import matplotlib.pyplot as plt # plt.imshow(np.concatenate((Normalize01(t2_slice), Normalize01(roi_slice)), axis=1), cmap='gray') # plt.show() from MeDIT.DataAugmentor import DataAugmentor2D, AugmentParametersGenerator param_dict = { 'stretch_x': 0.1, 'stretch_y': 0.1, 'shear': 0.1, 'rotate_z_angle': 20, 'horizontal_flip': True } augment_generator = AugmentParametersGenerator() augmentor = DataAugmentor2D() while True: augment_generator.RandomParameters(param_dict) transform_param = augment_generator.GetRandomParametersDict() print(transform_param) augment_t2 = augmentor.Execute(t2_slice, aug_parameter=transform_param, interpolation_method='linear') augment_roi = augmentor.Execute(roi_slice, aug_parameter=transform_param, interpolation_method='linear')
def ImageInImageOut2D(root_folder, input_shape, batch_size=8, augment_param={}): input_number, output_number = GetInputOutputNumber(root_folder) case_list = os.listdir(root_folder) input_list = [[] for index in range(input_number)] output_list = [[] for index in range(output_number)] param_generator = AugmentParametersGenerator() augmentor = DataAugmentor2D() while True: shuffle(case_list) for case in case_list: case_path = os.path.join(root_folder, case) if not case_path.endswith('.h5'): continue input_data_list, output_data_list = [], [] file = h5py.File(case_path, 'r') for input_number_index in range(input_number): input_data_list.append(file['input_' + str(input_number_index)]) for output_number_index in range(output_number): output_data_list.append(file['output_' + str(output_number_index)]) param_generator.RandomParameters(augment_param) augmentor.SetParameter(param_generator.GetRandomParametersDict()) input_data_list = AugmentDataList2D(input_data_list, augmentor) output_data_list = AugmentDataList2D(output_data_list, augmentor) input_data_list = CropDataList2D(input_data_list, input_shape) output_data_list = CropDataList2D(output_data_list, input_shape) AddOneSample(input_list, input_data_list) AddOneSample(output_list, output_data_list) if len(input_list[0]) >= batch_size: inputs = MakeKerasFormat(input_list) outputs = MakeKerasFormat(output_list) yield inputs, outputs input_list = [[] for index in range(input_number)] output_list = [[] for index in range(output_number)]
def GenerateMultiInputOneOutput_From2DMultiSliceTo2D(root_folder, input_shape, batch_size=8, augment_config='', is_yield=True): if augment_config: with open(augment_config, 'r') as file: random_params = json.load(file) param_generator = AugmentParametersGenerator() aug_generator = DataAugmentor2D() case_list = os.listdir(root_folder) shuffle(case_list) one_path = os.path.join(root_folder, case_list[0]) info = LoadH5InfoForGenerate(one_path) if info['input_number'] <= 1: print('Need Multi Input ', one_path) return input_list = [[] for temp in range(info['input_number'])] one_output = LoadH5(one_path, tag='output_0') output_array = np.zeros( (batch_size, input_shape[0], input_shape[1], one_output.shape[-1])) current_batch = 0 while True: shuffle(case_list) for case in case_list: case_path = os.path.join(root_folder, case) if augment_config: param_generator.RandomParameters(random_params) aug_generator.SetParameter( param_generator.GetRandomParametersDict()) for input_number in range(info['input_number']): data = LoadH5(case_path, tag='input_{:d}'.format(input_number)) crop_data = np.zeros( (input_shape[0], input_shape[1], data.shape[-1])) for slice_index in range(data.shape[-1]): if augment_config: aug_data = aug_generator.Execute( data[..., slice_index], interpolation_method='linear') crop_data[..., slice_index] = ExtractPatch( aug_data, patch_size=input_shape)[0] else: crop_data[..., slice_index] = ExtractPatch( data[..., slice_index], patch_size=input_shape)[0] input_list[input_number].append(crop_data) output_data = LoadH5(case_path, tag='output_0') if augment_config: aug_data = aug_generator.Execute(np.squeeze(output_data), interpolation_method='linear') output_array[current_batch, ..., 0] = ExtractPatch(aug_data, patch_size=input_shape)[0] else: output_array[current_batch, ..., 0] = ExtractPatch(np.squeeze(output_data), patch_size=input_shape)[0] current_batch += 1 if current_batch >= batch_size: input_list = [np.asarray(temp) for temp in input_list] return input_list, output_array current_batch = 0 # yield input_list, output_array input_list = [[] for temp in range(info['input_number'])] output_array = np.zeros((batch_size, input_shape[0], input_shape[1], one_output.shape[-1]))
def AugmentTrain(train_folder, batch_size): file_list = os.listdir(train_folder) image_list = [] label_list = [] for i in range(len(file_list)): # path data_path = os.path.join(train_folder, file_list[i]) h5_file = h5py.File(data_path, 'r') image = np.asarray(h5_file['input_0'], dtype=np.float32) label = np.asarray(h5_file['output_0'], dtype=np.uint8) # augmentation param param_dict = { 'stretch_x': 0.1, 'stretch_y': 0.1, 'shear': 0.1, 'rotate_z_angle': 20, 'horizontal_flip': True } # 设增强函数为augment_generator augment_generator = AugmentParametersGenerator() # 2D的数据增强 augmentor = DataAugmentor2D() augment_generator.RandomParameters(param_dict) transform_param = augment_generator.GetRandomParametersDict() augment_t2 = augmentor.Execute(image, aug_parameter=transform_param, interpolation_method='linear') augment_roi = augmentor.Execute(label, aug_parameter=transform_param, interpolation_method='linear') # cut if np.shape(augment_t2) == (440, 440): cropImage = image[100:340, 100:340] cropRoi = label[100:340, 100:340] cropaugmentImage = augment_t2[100:340, 100:340] cropaugmentRoi = augment_roi[100:340, 100:340] else: cropImage = image[60:300, 60:300] cropRoi = label[60:300, 60:300] cropaugmentImage = augment_t2[60:300, 60:300] cropaugmentRoi = augment_roi[60:300, 60:300] # one_hot roi_onehot = to_categorical(cropRoi) augment_roi_onehot = to_categorical(cropaugmentRoi) # show # plt.imshow(np.concatenate((Normalize01(cropImage), Normalize01(cropRoi)), axis=1), cmap='gray') # plt.show() # plt.imshow(np.concatenate((Normalize01(cropaugmentImage), Normalize01(cropaugmentRoi)), axis=1), cmap='gray') # plt.show() reshape = (240, 240, 1) cropImage = cropImage.reshape(reshape) cropaugmentImage = cropaugmentImage.reshape(reshape) # add data into list image_list.append(cropImage) label_list.append(roi_onehot) image_list.append(cropaugmentImage) label_list.append(augment_roi_onehot) if len(image_list) >= batch_size: yield np.asarray(image_list), np.asarray(label_list) image_list = [] label_list = []